Life science research has been undergoing a transition in recent years to large-scale experimentation, where a single project can require hundreds or thousands of measurements. Two fields that exemplify this trend are genomics and pharmaceutical drug screening. Researchers engaged in these fast growing areas need new and improved analytical systems that provide at least a ten-fold increase in the amount of data gathered as well as enhanced accuracy in the measurement of this data. To gain market acceptance, new products and systems also need to offer these benefits at attractive cost levels.
Genomics is the analysis of nucleic acids, which are the fundamental regulatory molecules of life. Nucleic acids take two forms, DNA and RNA. These molecules contain and convey the instructions that govern all cellular activities, including protein manufacture and cell reproduction. DNA and RNA consist of linear strands of nucleotide bases, commonly known as A's, G's, T's and C's, the specific sequences of which constitute the genetic information in the cell. The unique genetic blueprint for all living organisms, from bacteria to human beings, is encoded in the DNA. The entire DNA content of an organism is known as its genome, which is organized into functional units called genes. For a cell to read the genetic blueprint, the genetic information encoded in the DNA must first be copied to a specific type of RNA called messenger RNA or mRNA. The mRNA transmits this information throughout the cell and acts as the template for protein production. Proteins carry out the cellular functions encoded in the RNA copy of the DNA. Any defect or mutation in the sequence of nucleotide bases in the DNA or RNA can disrupt cell or protein function and lead to disease.
Genomics has created opportunities to fundamentally alter the field of human medicine through the discovery and development of novel drugs and an improved ability to diagnose and manage disease. Interest in understanding the relationships between genes and disease has generated a worldwide effort to identify and sequence the genes of many organisms, including the approximately three billion nucleotide pairs and the estimated 100,000 genes within the human genome. Once researchers identify the genes and their nucleotide sequences, it is anticipated that an understanding of the specific function of each of these genes and the role that different genes play in disease will require many years of additional research. Genomics also has applications in fields outside of human health care. For example, an improved understanding of plant and animal genomes will help to improve yields and productivity in the agriculture and livestock industries. The analysis of nucleic acids is also becoming increasingly important for industrial applications such as the testing of food, water and air.
The methods of analysis in the field of genomics generally fall into one of three major categories:
DNA Sequencing. DNA sequencing is the process of determining the linear order of nucleotide bases in a DNA fragment.
Genotyping. Genotyping refers to the identification of common variations in a sequence of DNA within a particular genome.
Gene Expression Analysis. Gene expression analysis involves measuring the expression of one or more genes in a specific cell or tissue.
Researchers today are utilizing all of these genomic analysis methods to understand genes, their function and genetic variability.
DNA sequencing is the process of determining the linear order of nucleotide bases in a strand of DNA and is performed with a laboratory instrument called a DNA sequencer. DNA sequencers use a technique known as electrophoresis, which uses an electric current to separate DNA molecules by size. This technique is also known as electrophoretic separation. In a DNA sequencer, the electric current causes smaller DNA molecules to move rapidly and larger DNA molecules to move more slowly. This enables the separation and ordering of complex mixtures of DNA molecules according to size, and thus allows the identification of the order of nucleotide bases.
Prior to beginning the DNA sequencing process, researchers typically must prepare the DNA samples. Preparation of a DNA sample for analysis includes manual and time-consuming laboratory processes such as centrifugation, filtration, measuring, mixing and dispensing. It is believed that sample preparation currently represents a major component of the time, labor and cost in sequencing. In addition, the manual nature of these steps renders sample preparation prone to human error, which can compromise the quality of information obtained from the sample. It is anticipated that integration and automation of these complex steps in a miniaturized format would significantly reduce the costs of sample preparation and improve data quality.
After sample preparation, researchers often analyze samples using one of the two leading types of DNA sequencers: gel-based sequencers and capillary array sequencers.
Gel-Based Sequencers. Until recently, all DNA sequencers used thin gels layered between two glass plates for performing electrophoresis. The throughput of a DNA sequencer is the number of DNA samples processed by the sequencer in a given amount of time. Throughput is determined by the time required for the electrophoretic separation and the number of DNA samples processed at one time. With early-generation DNA sequencers, the electrophoresis separation required 12 hours or longer and was limited to only 24 samples at a time.
Advanced generations of gel-based sequencers have reduced this separation time to approximately four hours and have allowed up to 96 samples to be processed at one time. While the throughput has increased with successive generations of gel-based sequencers, a significant amount of labor is still required to operate a gel-based sequencer. The labor involved in gel-based sequencers includes the time consuming tasks of preparing a new gel for each separation, loading each DNA sample onto the gel and cleaning the system after each separation.
Capillary Array Sequencers. In recent years, a number of companies have introduced a new generation of DNA sequencers, based on capillary electrophoresis. With capillary electrophoresis, each DNA sample is separated within a capillary, which is a small glass tube with the diameter of a human hair. In capillary array sequencers, up to 100 capillaries are bundled together to process many DNA samples simultaneously. Capillary array sequencers automate many of the labor-intensive steps in gel electrophoresis and provide significant improvements in operational efficiency. The time required for electrophoresis in a capillary array sequencer, however, is similar to that of current gel-based sequencers.
Advances in the performance of DNA sequencers generally have helped to rapidly expand the market for sequence information. In particular, the throughput of DNA sequencers has increased significantly over the last decade. This increase in throughput, along with improved automation, has substantially reduced the cost per unit of information obtained from DNA sequencers. These advances have enabled researchers to undertake large-scale sequencing projects that otherwise may not have been pursued. These include numerous projects underway to sequence entire genomes, including the human genome and various microbial, plant and animal genomes.
However, despite these advances in DNA sequencing technology, further improvements are required. Sequencing all of the DNA in a complex genome is a massive undertaking and, despite recent increases in throughput, requires up to hundreds of sequencers running in parallel for months or even years. In addition, the initial sequence of a genome typically contains errors, which then require additional sequencing to correct. To characterize the genetic diversity of an organism, researchers will need to sequence the genomes of many individuals and compare these sequences to identify differences. We also believe that researchers will want to sequence the genomes of more organisms as the cost of sequencing decreases. In summary, it is expected that the demand for DNA sequencing will continue to grow.
Genotyping is the process of analyzing locations within a genome where variations in a gene sequence, or genetic polymorphisms, are known to exist. Genetic polymorphisms play a role in an individual's susceptibility to disease and response to drugs. One type of polymorphism is a single nucleotide base variation, commonly referred to as a single nucleotide polymorphism, or SNP. Other types of variations involve changes in the length of simple repeating sequences and insertions or deletions of one or more bases at a particular location.
SNPs are the most common type of genetic variation. There are an estimated three to ten million SNPs in the human genome. While only a small fraction of human SNPs have been identified to date, we expect this number to increase dramatically during the next few years. For example, the SNP Consortium is a group of drug companies and public entities who are working together to discover 300,000 SNPs and contribute their findings to public databases. Numerous other individual companies have initiated programs to identify large numbers of human SNPs.
As more and more SNPs are identified, a new market is emerging for high throughput SNP genotyping. The simple identification of a SNP does not indicate whether or how it may relate to human health. To relate SNPs to disease or drag response, SNPs must be measured, or typed, in hundreds or thousands of people and correlated with clinical data describing the physical or mental health of those individuals. The emerging SNP genotyping market includes at least two segments:
Disease Association Studies. Disease association studies involve measuring specific sets of SNPs in healthy and diseased individuals to identify SNPs as markers for disease susceptibility and resistance. These studies could help researchers identify individuals who are at risk for such diseases as cardiovascular disease, hypertension, diabetes and cancer, and accelerate the discovery of new pharmaceuticals for these diseases. A single association study may involve typing up to 100,000 or more SNPs in thousands of individuals, requiring hundreds of millions of measurements.
Pharmacogenomics. Pharmacogenomics is the study of how individual genetic makeup influences drug response. The benefits of this knowledge include the potential for streamlining clinical trials by targeting a candidate drug to a specific responsive genotype, reducing both the cost and time of drug development. An additional benefit is the potential for tailoring drug prescriptions by genetic profile to maximize efficacy and minimize toxic side effects. Similar to disease association studies, a single clinical trial may require typing up to 100,000 or more SNPs in thousands of individuals.
Existing genotyping technologies do not provide the throughput, automation or economy needed for high throughput SNP analysis. Currently, the two leading techniques for SNP analysis are hybridization microarrays and enzyme detection methods.
Hybridization Microarrays. Hybridization microarrays are flat chips or glass slides which have different DNA fragments, or probes, located in known positions on the chip surface. Microarrays allow many SNPs to be measured at the same time on one DNA sample. This process of measuring multiple SNPs on one sample is called multiplexing. Researchers can only analyze one DNA sample on each microarray. Thus, microarrays offer a high degree of multiplexing but provide low sample throughput.
Enzyme Detection. Enzyme detection methods involve mixing a DNA sample with a specific enzyme and a DNA fragment of known sequence called a probe. There is one probe specific for each SNIP to be typed, and a signal generated during this reaction indicates the presence of a particular SNP. Researchers can perform these measurements in parallel using the current standard, microwell plates. Microwell plates are rectangular plastic plates which are roughly the size of a human hand and contain a number of small wells, each of which functions as a test tube. One advantage of this approach is that researchers can analyze different DNA samples in parallel on the same microwell plate. It is usually possible, however, to measure only a single SNP in each well. Thus, the overall throughput of enzyme methods is also relatively low.
Neither microarrays nor enzyme methods are ideal for high throughput SNP genotyping, where researchers need both high sample throughput and multiplexing capability, or the ability to measure multiple SNPs for each sample. New technologies are needed to meet the growing needs of this emerging market segment.
Gene expression analysis involves measuring the extent to which specific genes are expressed within a cell. A primary application of this process is differential gene expression analysis, where researchers compare the genes expressed in healthy and diseased samples to identify specific genes involved in a particular disease process. Another common application involves measuring a change in expression of certain genes when researchers add drug candidates to cells. As researchers identify more genes from the genome sequencing projects, the market for expression analysis technologies is expected to grow significantly.
The current leading technologies for gene expression analysis are the same as those previously described for genotyping. Researchers can use hybridization microarrays to monitor thousands of genes at the same time, but this approach is only feasible for relatively small numbers of samples, because only one DNA sample can be analyzed per individual microarray. Conversely, researchers can apply enzyme detection methods to large sample sets, but with that approach may measure only a single gene in each well of a microwell plate. It is submitted that neither of these approaches is suitable for measuring large numbers of genes over large numbers of DNA samples, as the testing of pharmaceutical drug candidates requires. A technology that could provide this capability would find rapid acceptance in the marketplace.
The genomics revolution is providing pharmaceutical researchers with a dramatic increase in the number of potential drug targets. A drug target is a molecule, usually a protein, which plays a role in a disease process and which researchers believe is a target for intervening in the disease process. In their search for new drugs, pharmaceutical researchers test many chemical compounds to determine whether they interact with drug targets. These researchers typically have large collections of chemical compounds to test against potential drug targets. In addition, in recent years pharmaceutical researchers have been vastly expanding the size of compound collections they use to screen against new drug targets. As a result, researchers require new laboratory technologies capable of screening increasingly large compound collections against an increasing number of drug targets in a cost-effective, automated and rapid manner. The market segments related to pharmaceutical drug screening are:
Assay Development. During the process of assay development, researchers develop methods for measuring the interaction of chemical compounds with specific drug targets.
Primary Screening. Primary screening involves testing entire compound collections against a drug target to identify “hits,” or those compounds which exhibit activity against a drug target.
Secondary Screening. Secondary screening includes performing follow-up testing to validate hits identified in primary screening and further characterize their feasibility as a drug.
To screen a compound collection against a new drug target, a researcher must develop a test, or assay, for measuring whether particular chemical compounds in the library interact with the drug target in a certain manner. The type of assay selected depends on the drug target under investigation and the type of information being sought. Researchers design some assays to measure whether and how tightly a compound binds to a drug target, such as the binding of a drug to a protein. Other assays are designed to measure whether and to what degree a compound reduces the biological activity of a drug target, such as the activity of an enzyme. In other cases, researchers test compound collections against living cells and measure a particular cellular response, such as a change in expression level of one or more genes.
Current assay development methods are time consuming, taking from weeks to months, and are labor intensive, largely due to the need to measure a particular molecule within a mixture of many different components. In addition, current technologies for performing assays provide only a fraction of the information needed for selecting potential drug candidates. For example, existing technologies only allow researchers to measure a single gene at one time for the purposes of monitoring gene expression. Existing detection methods also typically require preparation of reagents in a highly purified form, which requires additional time and labor.
Primary screening involves performing an identical test on each compound in a large collection to identify hits. Based on the size of most compound collections today, primary screening can involve hundreds of thousands of individual measurements against a single drug target. The time, expense and labor required to conduct a primary screen currently limits the number of screens that pharmaceutical researchers perform, and thereby limits their opportunities for discovering new drugs.
A major element of cost in primary screening comes from the amount of chemical and biochemical reagents, including the drug target, required to perform large numbers of assays. The amount of reagents required is related to the total number of measurements and the volume of each measurement. Because of the high cost and the limited availability of many reagents, researchers have attempted to reduce the total consumption of reagents by reducing the volume of each measurement from hundreds of microliters down to three to five microliters. A microliter is one millionth of a liter. The success of these efforts, however, has been limited by the effects of evaporation on small sample volumes, the sensitivity of existing detection methods and the difficulty of delivering small volumes of reagents to microwell plates with speed and precision. For example, a volume of one microliter can evaporate from an open well in a few minutes, and even a small amount of evaporation reduces the reliability and precision of a measurement. Furthermore, the detection capability of many assay methods becomes less sensitive as the test volume is reduced. Researchers can improve sensitivity by increasing the concentration of reagents. This conflicts, however, with the objective of reducing reagent consumption. Due to these difficulties in reducing assay volumes, it is thought that researchers still perform most assays in primary screening in volumes ranging from tens to hundreds of microliters. A reduction in assay volumes would allow researchers to investigate more drug targets and perform primary screens using larger compound collections.
Secondary screening involves performing a variety of measurements on each hit identified in a primary screen. While the number of compounds under investigation is smaller than in primary screening, the number and diversity of measurements performed on each compound is much larger. The purpose of these measurements is to verify and further characterize the biological activity of each hit. For example, researchers may test each hit against the drug target at different concentrations to determine its potency. Also, each hit may be tested against multiple enzymes to identify activity against any of these enzymes. Current technologies typically measure only a single data point at a time, such as the activity of one compound on a particular enzyme, limiting the efficiency and economy of secondary screening, as well as the efficiency of overall pharmaceutical research.
In vitro diagnostic testing is the process of analyzing constituents of blood, urine and other bodily fluids. The two largest categories of in vitro diagnostic test performed today are general chemistry and immunodiagnostic testing. General chemistry testing utilizes relatively simple chemical reactions to measure certain molecules found in relatively high concentration in certain bodily fluids (usually blood). The most commonly performed tests include measurement of glucose, cholesterol and triglyceride levels. In contrast, immunodiagnostic tests involve complex biological reactions e.g., heterogeneous immunoassays, and test for molecules which are found in very low concentrations.
Chemistry and immunology-based testing of patient blood using automated analyzer equipment accounted for more than 60% of all IVD testing in 1994. Chemistry and immunology-based testing of patient blood using automated analyzer equipment accounted for greater than 60% of all revenue generated by IVD testing in 1994. IVD tests are performed predominately in hospital testing laboratories and commercial testing facilities using automated analyzer equipment. Unlike clinical chemistry analyzers, which perform mostly blood chemistry tests, immunology analyzers are used in various testing laboratories and perform antibody-based testing of a wide variety of analytes. Immunodiagnostic tests utilize the function of natural human protein molecules called antibodies. Antibodies have the ability to recognize and bind to specific analytes such as bacteria, viruses and metabolites. Existing immunodiagnostic testing typically involves sophisticated instrumentation and multi-step protocols including sample dilution, variable incubation times and wash steps. Substantially all immunodiagnostic tests today are performed in centralized laboratories on complex instruments operated by skilled technicians.
As innovate and cost-effective technology becomes available, diagnostic testing is gradually migrating from high-volume clinical laboratories to point-of-care (POC) such as clinics, physician offices, homes, patient bedsides and emergency rooms. While clinical laboratories will continue to provide large volume testing, a new market is emerging for POC diagnostics which will provide for more frequent testing. POC testing eliminates the time and cost associated with utilizing remotely located laboratories, including those associated with specimen collection, preservation, transportation, processing and reporting of results. proprietary chemistry into microfluidic devices and sell value added products to R&D customers.
In addition to the existing companies that sell life science research products a new group of competitors has emerged that will also sell genomic-based products and these are generically termed biochip companies. Biochips encompasses a range of devices, some of which have little in common with semiconductor technology.
DNA chips are small flat surfaces on which strands of one-half of the DNA double-helix-called DNA probes or oligos are bound. Since one half of the DNA double-helix naturally bonds with its complementary other half-a process called hybridization-this type of chip can be used to identify the presence of particular genes in a biological sample. These chips, containing hundreds or thousands of unique DNA probes, are also called DNA microarrays and can be manufactured using a variety of techniques, including semiconductor processing technology, on a variety of surfaces, including glass and plastic.
The most common type of lab-on-a-chip uses microfluidics, a technique in which fluid samples move through tiny channels from one experimental site to another on the chip. The primary application for these devices is high-throughput screening, in which they are used to test biological samples more quickly at lower cost than conventional lab techniques.
Protein chips are similar to DNA chips except that they sample individual proteins that are coded for by the DNA. Sales of these devices is less than DNA chips because medical science is further from identifying and mapping all 100,000 to 150,000 proteins coded for by genomic DNA. The most significant and largest application for biochips is the use of DNA microarrays for expression profiling. In expression profiling, the chip is used to examine messenger RNA, which controls how different parts of the genes are turned on or off to create certain types of cells. If the gene is expressed one way, it may result in a normal muscle cell, for example. If it is expressed in another way, it may result in a turmor. By comparing these different expressions, researchers hope to discover ways to predict and perhaps prevent disease. Pharmacogenomics is a discipline that attempts to correlate a DNA pattern with the individual's response to drugs such as ability to metabolize a drug. The DNA pattern is obtained by studing single nucleotide polymorphisms (SNPs) that are found in all DNA. The clinical diagnostic applications of these technologies will follow and have major impact in cancer and genetic disease diagnosis although many believe that SNPs may be satisfactory to achieve patient profiling.
Single mode optical fibers have the unique feature of enhanced evanescent wave capability along with reduced mode cancellation that is seen with multimode waveguides. Previous work describes the use of tapered surfaces or fibers to conserve mode cancellation in multimode structures. The major problem with single mode systems is that the fiber or planar waveguides are very small in size as compared to a multimode waveguide system, making source and detector coupling very difficult. Multimode waveguides have typical sizes of 125 microns and larger, while single mode structures exist with typical sizes of 6 microns. The launching of light and overall manufacturing of system using single mode structures is difficult and expensive.
Total internal reflection (“TIR”) fluorescence detection has been shown to provide enhanced sensitivity of fluorescent moieties close to or on the surface. See for example the work of D. Modlin described in WO 004364. This technique is often used to determine fluorescent events in chemistries where the fluid itself is opaque to the excitation or emission wavelengths of light being used. However, the Modlin device and approach has some serious disadvantages including the need for highly specialized plates and machinery where alignment is critical. It is also requires comparatively large volumes if sample and analyses and is not providing the commercially practical solution which are still sought. The use of evanescent waveguides for analyze sensing has been demonstrated in optical fibers by the work of Myron Block and Thomas Herschfeld References listed below). R. Sutherland, J. Herron, and M. Feldstein have demonstrated analyte sensing in planar waveguides. U.S. Pat. No. 5,961,924 by Reichert et al. describes enhanced sensitivity by utilizing a step gradient waveguide allowing for femtomolar analyte detection. Confocal microscopy detection is often used to interrogate fluorescent signals matrixed on micro-arrays however such devices are disadvantageously expensive thereby limiting their commercial practicality in the clinical laboratory setting.
A confocal scanning microscopy system needs to scan the array surface to determine analyte fluorescence. A confocal scanner, such as that available from GS1 Lumonics, Inc., is capable of low-level detection but requires a scanning of the micro-array surface, determining where each spot is defined and reducing fluorescent or scatter background. The micro-array chemistry is spotted onto a solid surface by using one of several spotting techniques. A Cartesian Technologies spotter uses a series of pins to create individual spots.
The nature of the surface in which the spots are placed must be carefully known as the surface wetting characteristics will define the compactness of the spots on the array. If the spots are too close together on a highly wettable surface, cross-contamination occurs. Drop placement using the pin spotting is variable requiring the confocal microscopy scanner to always employ various algorithms to determine the spot locations. These requests of pre-scanning and precise location determination are preferably avoided.
Each micro-array system representing the current state of the art fails to adequately address the growing need for low-level detection, the control of individual micro-array spotted chemistries in a close packed density and a cost effective, manufacturable system.